1. Field of the Invention
The present invention relates to a method of processing and recognizing a digital document image to improve the image quality for outputting the image to a printer or displaying the image on a display device. The digital image of a printed document is input through an imaging device such as a scanner. The present invention also relates to a program for causing a computer to execute a document imaging operation to perform image processing, and to execute the method for the document image processing and recognition. The present invention further provides a device for performing the document image processing by the method, and a computer-readable recording medium on which the program is recorded.
2. Description of the Related Art
As color scanners and digital cameras have widely spread, printed color documents are often used as input information that is accumulated, output, and reused. Further, input image data is transmitted to remote places over a network. When digital color document image data is displayed or printed as it is without image processing, or is processed by an OCR (Optical Character Reader) or the like that optically reads in and recognizes the characters in the displayed image, the following phenomena are often observed.                The contrast between the characters and the background is weak.        The background, which should be white in the first place, is colored.        The background is smudged due to see-through images or noise.        Mutual interference among pixels causes a moire phenomenon that results in an evenly striped pattern.        Due to a low resolution, the visibility of small prints is low, and the recognition accuracy is low even after processing is performed with an OCR.        
So as to eliminate those problems, it is necessary to perform an image enhancing operation that is suitable for color document image data. Text image enhancing methods that have been disclosed as solutions can be classified into the following four types: tone correction such as contrast enhancement, filtering, model-based image restoration, and increasing resolution.
Tone correction can be performed to eliminate colors from the background, as well as to emphasize the contrast in image data (see Patent Document 1, and Non-Patent Document 1 and 2, for example). As for the filtering, noise eliminating methods utilizing morphology for extracting the features from binary image data or grayscale image data has been disclosed (see Non-Patent Documents 3 and 4, for example). As another example of the filtering method, a method of eliminating noise using a secondary filter without blurring the details has been disclosed (see Non-Patent Document 5, for example).
The cause of a moire phenomenon is the halftone color portion that is represented as a dot pattern in image data. However, a moire phenomenon can be avoided by converting the halftone color portion into continuous tone representation (see Patent Document 2, for example). It is also effective to employ the model-based image restoration, such as a method of restoring an image by carrying out cluster analysis on the causes of OCR recognition errors and classifying the causes into models (see Non-Patent Document 6, for example).
To increase the character visibility and the accuracy of an OCR, it is effective to increase the resolution of low-resolution character image data. To achieve this, the following methods have been disclosed: a method of generating the outline for a desired resolution by averaging the character bitmap (data that represents each image as arrays of colored dots) through clustering (see Patent Document 3 and Non-Patent Document 7, for example); a method of restoring optimum high-resolution image data by formulating it as an inverse problem based on the evaluation function formed with the three criteria of distribution diphasic, smoothness, and brightness (see Non-Patent Documents 8 and 9, for example); and a method of increasing a resolution and binarizing data through interpolation (see Patent Documents 4 through 7, and Non-Patent Documents 10 and 11, for example). By performing various operations on color document image data as above, optimum image data for various purposes of use, such as display, printing, and OCR, can be generated.
Recently, other than the generation of binary image data suitable for OCRs and raster image data suitable for printing, new purposes of use have emerged for digitized images. For example, the following techniques have been disclosed: a novel image data file representing technique of representing image data based on “Mixed Raster Content Model” that divides the image data into the background (reduced to a low resolution), the foreground such as characters (of the original resolution or a higher resolution), and the colors of the foreground (such as a character color palette); and a technique of rearranging and reconstructing the constituent elements of characters and pictures obtained from scanned image data, so as to turn them into a format suitable for browsing on a screen using HTML (see Non-Patent Document 12, for example).
The following is a list of the above mentioned reference documents:                Patent Document 1: U.S. Pat. No. 5,524,070        Patent Document 2: Japanese Laid-Open Patent Application No. 2003-281526        Patent Document 3: U.S. Pat. No. 5,930,393        Patent Document 4: Japanese Patent No. 3345350        Patent Document 5: Japanese Laid-Open Patent Application No. 8-340446        Patent Document 6: Japanese Laid-Open Patent Application No. 2001-118032        U.S. Pat. No. 6,347,156        Non-Patent Document 1: Y. C. Shin, et al., “Contrast Enhancement of Mail Piece Images”, (USA), Proceedings of SPIE, 1992, vol. 1661, pp. 27-37        Non-Patent Document 2: Y. C. Shin, et al., “Enhancement of Mail Piece Images Based on Window Statistics”, (USA), Proceedings of SPIE, 1993, vol. 1906, pp. 37-48        Non-Patent Document 3: L. Koskinen, et al., “Text Enhancement Method Based on Soft Morphological Filters”, (USA), Proceedings of SPIE, 1994, vol. 2181, pp. 243-253        Non-Patent Document 4: J. Liang, et al., “Document Image Restoration Using Binary Morphological Filters”, (USA), Proceedings of SPIE, 1996, vol. 2660, pp. 274-285        Non-Patent Document 5: G. Ramponi, et al., “Enhancing Document Images with a Quadratic Filter”, (USA), Signal Processing, 1993, vol. 33, pp. 23-34        Non-Patent Document 6: M. Y. Jaisimha, et al., “Model-Based Restoration of Document Images for OCR”, (USA), Proceedings of SPIE, 1996, vol. 2660, pp. 297-308        Non-Patent Document 7: J. D. Hobby, et al., “Enhancing Degraded Document Images via Bitmap Clustering and Averaging”, (Germany), Proceeding of 4th International Conference on Document Analysis and Recognition, August 1997        Non-Patent Document 8: P. D. Thouin, et al., “A Method for Restoration of Low-Resolution Document Images”, (USA), International Journal on Document Analysis and Recognition, 2000, vol. 2, pp. 200-210        Non-Patent Document 9: P. D. Thouin, et al., “Automated System for Restoration of Low-Resolution Document and Text Images”, (USA), Journal of Electronic Imaging, 2001, vol. 10, No. 2, pp. 535-547        Non-Patent Document 10: M. J. Taylor, et al., “Enhancement of Document Images from Cameras”, (USA), Proceedings of SPIE, 1998, vol. 3305, pp. 230-241        Non-Patent Document 11: H. Li, et al., “Text Enhancement in Digital Video”, (USA), Proceedings of SPIE, 1999, vol. 3651, pp. 2-9        Non-Patent Document 12: T. Breuel, et al., “Paper to PDA”, (Canada), Proceeding of 16th International Conference on Pattern Recognition, August 2002        
In a case where the number of data formats (purposes of use) designated by users for color document image data is small, several operations are performed on input image data in a pipeline fashion, so as to obtain resultant image data. However, as the purposes of use have become more varied, the following problems have arisen:                1) A load and waste are caused in the application program for each purpose of use.        Since the same procedure is called up for each purpose of use, unnecessary operations need to be performed.        The intermediate data that is necessary for “Undo” and “Redo” of procedures needs to be managed separately for each purpose of use.        The history and conditions of operations need to be managed separately for each purpose of use.        
2) Enhancement and system maintenance become difficult.                When a new operation for generating optimum image data for a certain purpose of use is employed, or a new purpose of use, a new file format, or a new image data representing format is introduced, it is necessary to cope with the new operation or format in the individual application program for each purpose of use.        The file format or image representing format associated with each purpose of use might be mistaken for the procedure for generating an optimum image. To counter this problem, application programs with low modularity need to be produced in the same number as the number of file formats or image representing formats.        